{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 多特征的时序数据的模型拟合\n",
    "- 轨迹数据范围：2021-12-23 至 2022-2-01\n",
    "- 患者数据范围：2021-1-8 至 2022-2-15\n",
    "- 拟合目标：累计确诊人数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tensorflow.python.keras.models import Sequential\n",
    "from tensorflow.python.keras.layers import LSTM\n",
    "from tensorflow.python.keras.layers import Dense, Dropout\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>in_hospital</th>\n",
       "      <th>acc_cured</th>\n",
       "      <th>loc</th>\n",
       "      <th>catering</th>\n",
       "      <th>community</th>\n",
       "      <th>transportation</th>\n",
       "      <th>shop</th>\n",
       "      <th>work</th>\n",
       "      <th>entertainment</th>\n",
       "      <th>education</th>\n",
       "      <th>hotel</th>\n",
       "      <th>medical</th>\n",
       "      <th>sight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021/12/23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021/12/24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>84</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021/12/25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>114</td>\n",
       "      <td>8</td>\n",
       "      <td>26</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021/12/26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>120</td>\n",
       "      <td>12</td>\n",
       "      <td>54</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021/12/27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>160</td>\n",
       "      <td>30</td>\n",
       "      <td>46</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  new_confirmed  in_hospital  acc_cured  loc  catering  \\\n",
       "0  2021/12/23              0            0          0    0         0   \n",
       "1  2021/12/24              0            0          0    2         2   \n",
       "2  2021/12/25              0            0          0    5         2   \n",
       "3  2021/12/26              0            0          0    9        12   \n",
       "4  2021/12/27              0            0          0   18         6   \n",
       "\n",
       "   community  transportation  shop  work  entertainment  education  hotel  \\\n",
       "0         54              24    10    10              0         26      0   \n",
       "1         84               8    18    18              2         42      0   \n",
       "2        114               8    26    18              6          6      0   \n",
       "3        120              12    54    12              2         16      0   \n",
       "4        160              30    46    30              2         94      2   \n",
       "\n",
       "   medical  sight  \n",
       "0        4      0  \n",
       "1        4      0  \n",
       "2        4      0  \n",
       "3       10      0  \n",
       "4       10      2  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read data\n",
    "df = pd.read_csv('feature.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40   2022-02-01\n",
      "41   2022-02-02\n",
      "42   2022-02-03\n",
      "43   2022-02-04\n",
      "44   2022-02-05\n",
      "45   2022-02-06\n",
      "46   2022-02-07\n",
      "47   2022-02-08\n",
      "48   2022-02-09\n",
      "49   2022-02-10\n",
      "50   2022-02-11\n",
      "51   2022-02-12\n",
      "52   2022-02-13\n",
      "53   2022-02-14\n",
      "54   2022-02-15\n",
      "Name: date, dtype: datetime64[ns]\n",
      "['new_confirmed', 'in_hospital', 'acc_cured', 'loc', 'catering', 'community', 'transportation', 'shop', 'work', 'entertainment', 'education', 'hotel', 'medical', 'sight']\n"
     ]
    }
   ],
   "source": [
    "# separate dates for future plotting\n",
    "train_dates = pd.to_datetime(df['date'])\n",
    "print(train_dates.tail(15)) # Check last few dates. \n",
    "\n",
    "# variables for training\n",
    "cols = list(df)[1:15]\n",
    "print(cols) # check features\n",
    "\n",
    "# New dataframe with only training data - 14 columns\n",
    "df_for_training = df[cols].astype(float)\n",
    "\n",
    "# LSTM uses sigmoid and tanh that are sensitive to magnitude so values need to be normalized\n",
    "# normalize the dataset\n",
    "scaler = StandardScaler()\n",
    "scaler = scaler.fit(df_for_training)\n",
    "df_for_training_scaled = scaler.transform(df_for_training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainX shape == (48, 7, 14).\n",
      "trainY shape == (48, 1).\n"
     ]
    }
   ],
   "source": [
    "#As required for LSTM networks, we require to reshape an input data into n_samples x timesteps x n_features. \n",
    "\n",
    "#Empty lists to be populated using formatted training data\n",
    "trainX = []\n",
    "trainY = []\n",
    "\n",
    "n_future = 1   # Number of days we want to look into the future based on the past days.\n",
    "n_past = 7  # Number of past days we want to use to predict the future.\n",
    "\n",
    "#Reformat input data into a shape: (n_samples x timesteps x n_features)\n",
    "for i in range(n_past, len(df_for_training_scaled) - n_future +1):\n",
    "    trainX.append(df_for_training_scaled[i - n_past:i, 0:df_for_training.shape[1]])\n",
    "    trainY.append(df_for_training_scaled[i + n_future - 1:i + n_future, 0])\n",
    "\n",
    "trainX, trainY = np.array(trainX), np.array(trainY)\n",
    "print('trainX shape == {}.'.format(trainX.shape))\n",
    "print('trainY shape == {}.'.format(trainY.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-11-06 20:56:54.586921: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_SYSTEM_DRIVER_MISMATCH: system has unsupported display driver / cuda driver combination\n",
      "2022-11-06 20:56:54.586983: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: bill-Lenovo-V15-IWL\n",
      "2022-11-06 20:56:54.586991: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: bill-Lenovo-V15-IWL\n",
      "2022-11-06 20:56:54.587257: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 515.65.1\n",
      "2022-11-06 20:56:54.587285: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 515.76.0\n",
      "2022-11-06 20:56:54.587290: E tensorflow/stream_executor/cuda/cuda_diagnostics.cc:313] kernel version 515.76.0 does not match DSO version 515.65.1 -- cannot find working devices in this configuration\n",
      "2022-11-06 20:56:54.587906: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 7, 64)             20224     \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 32)                12416     \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 32,673\n",
      "Trainable params: 32,673\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# define the Autoencoder model\n",
    "\n",
    "model = Sequential()\n",
    "model.add(LSTM(64, activation='relu', input_shape=(trainX.shape[1], trainX.shape[2]), return_sequences=True))\n",
    "model.add(LSTM(32, activation='relu', return_sequences=False))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(trainY.shape[1]))\n",
    "\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-11-06 20:56:55.048606: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 4s 199ms/step - loss: 1.1889 - val_loss: 0.3139\n",
      "Epoch 2/300\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 1.0654 - val_loss: 0.2699\n",
      "Epoch 3/300\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 1.0275 - val_loss: 0.2384\n",
      "Epoch 4/300\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 0.8708 - val_loss: 0.2086\n",
      "Epoch 5/300\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 0.7630 - val_loss: 0.1884\n",
      "Epoch 6/300\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 0.6140 - val_loss: 0.1655\n",
      "Epoch 7/300\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 0.4883 - val_loss: 0.1397\n",
      "Epoch 8/300\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 0.5218 - val_loss: 0.1095\n",
      "Epoch 9/300\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 0.3994 - val_loss: 0.0783\n",
      "Epoch 10/300\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 0.4201 - val_loss: 0.0486\n",
      "Epoch 11/300\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 0.3967 - val_loss: 0.0266\n",
      "Epoch 12/300\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 0.2844 - val_loss: 0.0166\n",
      "Epoch 13/300\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 0.4152 - val_loss: 0.0110\n",
      "Epoch 14/300\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 0.2660 - val_loss: 0.0062\n",
      "Epoch 15/300\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 0.2204 - val_loss: 0.0029\n",
      "Epoch 16/300\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 0.3617 - val_loss: 7.9143e-04\n",
      "Epoch 17/300\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 0.3670 - val_loss: 9.3737e-04\n",
      "Epoch 18/300\n",
      "3/3 [==============================] - 0s 28ms/step - loss: 0.4391 - val_loss: 0.0013\n",
      "Epoch 19/300\n",
      "3/3 [==============================] - 0s 29ms/step - loss: 0.3314 - val_loss: 0.0017\n",
      "Epoch 20/300\n",
      "3/3 [==============================] - 0s 24ms/step - loss: 0.3274 - val_loss: 8.8943e-04\n",
      "Epoch 21/300\n",
      "3/3 [==============================] - 0s 29ms/step - loss: 0.2813 - val_loss: 6.9260e-04\n",
      "Epoch 22/300\n",
      "3/3 [==============================] - 0s 28ms/step - loss: 0.3947 - val_loss: 0.0012\n",
      "Epoch 23/300\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 0.2460 - val_loss: 0.0012\n",
      "Epoch 24/300\n",
      "3/3 [==============================] - 0s 26ms/step - loss: 0.2516 - val_loss: 6.9275e-04\n",
      "Epoch 25/300\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 0.3222 - val_loss: 0.0015\n",
      "Epoch 26/300\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 0.2961 - val_loss: 0.0044\n",
      "Epoch 27/300\n",
      "3/3 [==============================] - 0s 26ms/step - loss: 0.4617 - val_loss: 0.0044\n",
      "Epoch 28/300\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 0.2392 - val_loss: 0.0049\n",
      "Epoch 29/300\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 0.1969 - val_loss: 0.0036\n",
      "Epoch 30/300\n",
      "3/3 [==============================] - 0s 33ms/step - loss: 0.1890 - val_loss: 0.0024\n",
      "Epoch 31/300\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 0.3002 - val_loss: 0.0030\n",
      "Epoch 32/300\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 0.1749 - val_loss: 0.0052\n",
      "Epoch 33/300\n",
      "3/3 [==============================] - 0s 28ms/step - loss: 0.1727 - val_loss: 0.0033\n",
      "Epoch 34/300\n",
      "3/3 [==============================] - 0s 29ms/step - loss: 0.2578 - val_loss: 0.0016\n",
      "Epoch 35/300\n",
      "3/3 [==============================] - 0s 29ms/step - loss: 0.2764 - val_loss: 0.0014\n",
      "Epoch 36/300\n",
      "3/3 [==============================] - 0s 33ms/step - loss: 0.3468 - val_loss: 0.0014\n",
      "Epoch 37/300\n",
      "3/3 [==============================] - 0s 29ms/step - loss: 0.2833 - val_loss: 0.0016\n",
      "Epoch 38/300\n",
      "3/3 [==============================] - 0s 29ms/step - loss: 0.2108 - val_loss: 0.0016\n",
      "Epoch 39/300\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 0.2001 - val_loss: 0.0032\n",
      "Epoch 40/300\n",
      "3/3 [==============================] - 0s 29ms/step - loss: 0.2433 - val_loss: 0.0064\n",
      "Epoch 41/300\n",
      "3/3 [==============================] - 0s 60ms/step - loss: 0.2120 - val_loss: 0.0053\n",
      "Epoch 42/300\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.3649 - val_loss: 0.0073\n",
      "Epoch 43/300\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 0.2273 - val_loss: 0.0089\n",
      "Epoch 44/300\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 0.1951 - val_loss: 0.0045\n",
      "Epoch 45/300\n",
      "3/3 [==============================] - 0s 27ms/step - loss: 0.2761 - val_loss: 7.8384e-04\n",
      "Epoch 46/300\n",
      "3/3 [==============================] - 0s 33ms/step - loss: 0.1996 - val_loss: 9.2584e-04\n",
      "Epoch 47/300\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 0.1736 - val_loss: 0.0013\n",
      "Epoch 48/300\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 0.2249 - val_loss: 9.8113e-04\n",
      "Epoch 49/300\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 0.1429 - val_loss: 0.0010\n",
      "Epoch 50/300\n",
      "3/3 [==============================] - 0s 27ms/step - loss: 0.2628 - val_loss: 0.0017\n",
      "Epoch 51/300\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 0.1616 - val_loss: 0.0017\n",
      "Epoch 52/300\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 0.1517 - val_loss: 0.0010\n",
      "Epoch 53/300\n",
      "3/3 [==============================] - 0s 33ms/step - loss: 0.2414 - val_loss: 7.3189e-04\n",
      "Epoch 54/300\n",
      "3/3 [==============================] - 0s 28ms/step - loss: 0.1907 - val_loss: 0.0018\n",
      "Epoch 55/300\n",
      "3/3 [==============================] - 0s 24ms/step - loss: 0.1221 - val_loss: 0.0049\n",
      "Epoch 56/300\n",
      "3/3 [==============================] - 0s 26ms/step - loss: 0.1754 - val_loss: 0.0071\n",
      "Epoch 57/300\n",
      "3/3 [==============================] - 0s 27ms/step - loss: 0.2207 - val_loss: 0.0051\n",
      "Epoch 58/300\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 0.1094 - val_loss: 0.0017\n",
      "Epoch 59/300\n",
      "3/3 [==============================] - 0s 24ms/step - loss: 0.1003 - val_loss: 7.0813e-04\n",
      "Epoch 60/300\n",
      "3/3 [==============================] - 0s 24ms/step - loss: 0.1016 - val_loss: 0.0012\n",
      "Epoch 61/300\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 0.2407 - val_loss: 7.6596e-04\n",
      "Epoch 62/300\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 0.0651 - val_loss: 0.0013\n",
      "Epoch 63/300\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 0.1128 - val_loss: 0.0071\n",
      "Epoch 64/300\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 0.0813 - val_loss: 0.0150\n",
      "Epoch 65/300\n",
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      "3/3 [==============================] - 0s 32ms/step - loss: 0.0408 - val_loss: 0.0014\n",
      "Epoch 232/300\n",
      "3/3 [==============================] - 0s 33ms/step - loss: 0.0163 - val_loss: 0.0027\n",
      "Epoch 233/300\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.0249 - val_loss: 0.0052\n",
      "Epoch 234/300\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 0.0416 - val_loss: 0.0057\n",
      "Epoch 235/300\n",
      "3/3 [==============================] - 0s 27ms/step - loss: 0.0251 - val_loss: 0.0044\n",
      "Epoch 236/300\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 0.0347 - val_loss: 0.0027\n",
      "Epoch 237/300\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 0.0450 - val_loss: 0.0015\n",
      "Epoch 238/300\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.1015 - val_loss: 7.1961e-04\n",
      "Epoch 239/300\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.0224 - val_loss: 7.1549e-04\n",
      "Epoch 240/300\n",
      "3/3 [==============================] - 0s 33ms/step - loss: 0.0123 - val_loss: 7.4363e-04\n",
      "Epoch 241/300\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.0528 - val_loss: 7.3367e-04\n",
      "Epoch 242/300\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.0175 - val_loss: 7.1788e-04\n",
      "Epoch 243/300\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 0.0342 - val_loss: 7.3276e-04\n",
      "Epoch 244/300\n",
      "3/3 [==============================] - 0s 61ms/step - loss: 0.1396 - val_loss: 7.4999e-04\n",
      "Epoch 245/300\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.0541 - val_loss: 0.0011\n",
      "Epoch 246/300\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 0.0634 - val_loss: 0.0013\n",
      "Epoch 247/300\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.1319 - val_loss: 9.3344e-04\n",
      "Epoch 248/300\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 0.0234 - val_loss: 7.9119e-04\n",
      "Epoch 249/300\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 0.0236 - val_loss: 7.2309e-04\n",
      "Epoch 250/300\n",
      "3/3 [==============================] - 0s 28ms/step - loss: 0.0384 - val_loss: 0.0012\n",
      "Epoch 251/300\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 0.0358 - val_loss: 0.0026\n",
      "Epoch 252/300\n",
      "3/3 [==============================] - 0s 32ms/step - loss: 0.0154 - val_loss: 0.0028\n",
      "Epoch 253/300\n",
      "3/3 [==============================] - 0s 28ms/step - loss: 0.0477 - val_loss: 0.0017\n",
      "Epoch 254/300\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 0.0307 - val_loss: 8.9769e-04\n",
      "Epoch 255/300\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 0.0295 - val_loss: 8.5629e-04\n",
      "Epoch 256/300\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 0.0238 - val_loss: 0.0018\n",
      "Epoch 257/300\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 0.0250 - val_loss: 0.0025\n",
      "Epoch 258/300\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 0.0218 - val_loss: 0.0017\n",
      "Epoch 259/300\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 0.0261 - val_loss: 7.1419e-04\n",
      "Epoch 260/300\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 0.0225 - val_loss: 7.9792e-04\n",
      "Epoch 261/300\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 0.0231 - val_loss: 7.5424e-04\n",
      "Epoch 262/300\n",
      "3/3 [==============================] - 0s 24ms/step - loss: 0.0206 - val_loss: 9.7643e-04\n",
      "Epoch 263/300\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 0.0180 - val_loss: 0.0016\n",
      "Epoch 264/300\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 0.0279 - val_loss: 9.8048e-04\n",
      "Epoch 265/300\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 0.0179 - val_loss: 7.3816e-04\n",
      "Epoch 266/300\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 0.0193 - val_loss: 0.0012\n",
      "Epoch 267/300\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 0.0221 - val_loss: 0.0022\n",
      "Epoch 268/300\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 0.0318 - val_loss: 0.0018\n",
      "Epoch 269/300\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 0.0173 - val_loss: 8.7118e-04\n",
      "Epoch 270/300\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 0.0244 - val_loss: 9.6772e-04\n",
      "Epoch 271/300\n",
      "3/3 [==============================] - 0s 24ms/step - loss: 0.0224 - val_loss: 0.0016\n",
      "Epoch 272/300\n",
      "3/3 [==============================] - 0s 28ms/step - loss: 0.0217 - val_loss: 0.0019\n",
      "Epoch 273/300\n",
      "3/3 [==============================] - 0s 32ms/step - loss: 0.0151 - val_loss: 0.0011\n",
      "Epoch 274/300\n",
      "3/3 [==============================] - 0s 26ms/step - loss: 0.0231 - val_loss: 9.7025e-04\n",
      "Epoch 275/300\n",
      "3/3 [==============================] - 0s 24ms/step - loss: 0.0254 - val_loss: 8.3573e-04\n",
      "Epoch 276/300\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 0.0423 - val_loss: 8.0766e-04\n",
      "Epoch 277/300\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 0.0480 - val_loss: 8.3204e-04\n",
      "Epoch 278/300\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 0.0362 - val_loss: 7.5679e-04\n",
      "Epoch 279/300\n",
      "3/3 [==============================] - 0s 20ms/step - loss: 0.0863 - val_loss: 7.1758e-04\n",
      "Epoch 280/300\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 0.0320 - val_loss: 7.5352e-04\n",
      "Epoch 281/300\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 0.0432 - val_loss: 7.6623e-04\n",
      "Epoch 282/300\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 0.0210 - val_loss: 7.4709e-04\n",
      "Epoch 283/300\n",
      "3/3 [==============================] - 0s 28ms/step - loss: 0.0384 - val_loss: 0.0016\n",
      "Epoch 284/300\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 0.0319 - val_loss: 0.0011\n",
      "Epoch 285/300\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 0.0537 - val_loss: 7.2832e-04\n",
      "Epoch 286/300\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 0.0360 - val_loss: 8.7036e-04\n",
      "Epoch 287/300\n",
      "3/3 [==============================] - 0s 21ms/step - loss: 0.0341 - val_loss: 7.6802e-04\n",
      "Epoch 288/300\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 0.0202 - val_loss: 8.7750e-04\n",
      "Epoch 289/300\n",
      "3/3 [==============================] - 0s 26ms/step - loss: 0.0444 - val_loss: 0.0016\n",
      "Epoch 290/300\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 0.1219 - val_loss: 0.0011\n",
      "Epoch 291/300\n",
      "3/3 [==============================] - 0s 24ms/step - loss: 0.0685 - val_loss: 7.1148e-04\n",
      "Epoch 292/300\n",
      "3/3 [==============================] - 0s 24ms/step - loss: 0.1579 - val_loss: 9.3074e-04\n",
      "Epoch 293/300\n",
      "3/3 [==============================] - 0s 31ms/step - loss: 0.0325 - val_loss: 0.0011\n",
      "Epoch 294/300\n",
      "3/3 [==============================] - 0s 27ms/step - loss: 0.0233 - val_loss: 8.4299e-04\n",
      "Epoch 295/300\n",
      "3/3 [==============================] - 0s 24ms/step - loss: 0.0353 - val_loss: 7.1392e-04\n",
      "Epoch 296/300\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 0.0202 - val_loss: 0.0011\n",
      "Epoch 297/300\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 0.0721 - val_loss: 0.0012\n",
      "Epoch 298/300\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 0.0188 - val_loss: 0.0013\n",
      "Epoch 299/300\n",
      "3/3 [==============================] - 0s 25ms/step - loss: 0.0460 - val_loss: 9.4247e-04\n",
      "Epoch 300/300\n",
      "3/3 [==============================] - 0s 23ms/step - loss: 0.0315 - val_loss: 7.1378e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0369ff1c40>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fit the model\n",
    "history = model.fit(trainX, trainY, epochs=300, batch_size=16, validation_split=0.1, verbose=1)\n",
    "\n",
    "plt.plot(history.history['loss'], label='Training loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation loss')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 测试模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Timestamp('2021-12-30 00:00:00', freq='D'), Timestamp('2021-12-31 00:00:00', freq='D'), Timestamp('2022-01-01 00:00:00', freq='D'), Timestamp('2022-01-02 00:00:00', freq='D'), Timestamp('2022-01-03 00:00:00', freq='D'), Timestamp('2022-01-04 00:00:00', freq='D'), Timestamp('2022-01-05 00:00:00', freq='D'), Timestamp('2022-01-06 00:00:00', freq='D'), Timestamp('2022-01-07 00:00:00', freq='D'), Timestamp('2022-01-08 00:00:00', freq='D'), Timestamp('2022-01-09 00:00:00', freq='D'), Timestamp('2022-01-10 00:00:00', freq='D'), Timestamp('2022-01-11 00:00:00', freq='D'), Timestamp('2022-01-12 00:00:00', freq='D'), Timestamp('2022-01-13 00:00:00', freq='D'), Timestamp('2022-01-14 00:00:00', freq='D'), Timestamp('2022-01-15 00:00:00', freq='D'), Timestamp('2022-01-16 00:00:00', freq='D'), Timestamp('2022-01-17 00:00:00', freq='D'), Timestamp('2022-01-18 00:00:00', freq='D'), Timestamp('2022-01-19 00:00:00', freq='D'), Timestamp('2022-01-20 00:00:00', freq='D'), Timestamp('2022-01-21 00:00:00', freq='D'), Timestamp('2022-01-22 00:00:00', freq='D'), Timestamp('2022-01-23 00:00:00', freq='D'), Timestamp('2022-01-24 00:00:00', freq='D'), Timestamp('2022-01-25 00:00:00', freq='D'), Timestamp('2022-01-26 00:00:00', freq='D'), Timestamp('2022-01-27 00:00:00', freq='D'), Timestamp('2022-01-28 00:00:00', freq='D'), Timestamp('2022-01-29 00:00:00', freq='D'), Timestamp('2022-01-30 00:00:00', freq='D'), Timestamp('2022-01-31 00:00:00', freq='D'), Timestamp('2022-02-01 00:00:00', freq='D'), Timestamp('2022-02-02 00:00:00', freq='D'), Timestamp('2022-02-03 00:00:00', freq='D'), Timestamp('2022-02-04 00:00:00', freq='D'), Timestamp('2022-02-05 00:00:00', freq='D'), Timestamp('2022-02-06 00:00:00', freq='D'), Timestamp('2022-02-07 00:00:00', freq='D'), Timestamp('2022-02-08 00:00:00', freq='D'), Timestamp('2022-02-09 00:00:00', freq='D'), Timestamp('2022-02-10 00:00:00', freq='D'), Timestamp('2022-02-11 00:00:00', freq='D'), Timestamp('2022-02-12 00:00:00', freq='D'), Timestamp('2022-02-13 00:00:00', freq='D'), Timestamp('2022-02-14 00:00:00', freq='D'), Timestamp('2022-02-15 00:00:00', freq='D')]\n"
     ]
    }
   ],
   "source": [
    "#Predicting...\n",
    "#Remember that we can only predict one day in future as our model needs 5 variables\n",
    "#as inputs for prediction. We only have all 5 variables until the last day in our dataset.\n",
    "n_past_days = len(train_dates)-n_past\n",
    "n_days_for_prediction=n_past_days  # repredict \n",
    "\n",
    "predict_period_dates = pd.date_range(list(train_dates)[-n_past_days], periods=n_days_for_prediction,).tolist()\n",
    "print(predict_period_dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make prediction\n",
    "prediction = model.predict(trainX[-n_days_for_prediction-n_past:]) #shape = (n, 1) where n is the n_days_for_prediction\n",
    "\n",
    "#Perform inverse transformation to rescale back to original range\n",
    "#Since we used n_feature variables for transform, the inverse expects same dimensions\n",
    "#Therefore, let us copy our values n_feature times and discard them after inverse transform\n",
    "prediction_copies = np.repeat(prediction, df_for_training.shape[1], axis=-1)\n",
    "y_pred_future = scaler.inverse_transform(prediction_copies)[:,0]\n",
    "\n",
    "# Convert timestamp to date\n",
    "forecast_dates = []\n",
    "for time_i in predict_period_dates:\n",
    "    forecast_dates.append(time_i.date())\n",
    "    \n",
    "df_forecast = pd.DataFrame({'date':np.array(forecast_dates), 'acc_confirmed':y_pred_future})\n",
    "df_forecast['date']=pd.to_datetime(forecast_dates)\n",
    "\n",
    "# extract original data\n",
    "original = df.copy()[['date', 'acc_confirmed']]\n",
    "original['date']=pd.to_datetime(original['date'])\n",
    "\n",
    "# locate the range of dates\n",
    "original = original.loc[original['date'] >= '2022-01-08']\n",
    "df_forecast = df_forecast.loc[df_forecast['date'] >= '2022-01-08']\n",
    "plt.figure()\n",
    "plt.plot(original['date'], original['acc_confirmed'], label='original')\n",
    "plt.plot(df_forecast['date'], df_forecast['acc_confirmed'], label='forecast')\n",
    "plt.xticks(rotation=300)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 模型的拟合效果看起来还是比较理想的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>acc_confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2022-01-08</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2022-01-09</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2022-01-10</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2022-01-11</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2022-01-12</td>\n",
       "      <td>101</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2022-01-13</td>\n",
       "      <td>142</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2022-01-14</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2022-01-15</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2022-01-16</td>\n",
       "      <td>294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2022-01-17</td>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2022-01-18</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2022-01-19</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2022-01-20</td>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2022-01-21</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2022-01-22</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2022-01-23</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2022-01-24</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2022-01-25</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2022-01-26</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2022-01-27</td>\n",
       "      <td>366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2022-01-28</td>\n",
       "      <td>371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2022-01-29</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2022-01-30</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2022-01-31</td>\n",
       "      <td>393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2022-02-02</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2022-02-03</td>\n",
       "      <td>418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2022-02-04</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2022-02-05</td>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2022-02-06</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2022-02-07</td>\n",
       "      <td>424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2022-02-08</td>\n",
       "      <td>424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2022-02-09</td>\n",
       "      <td>424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2022-02-10</td>\n",
       "      <td>424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2022-02-11</td>\n",
       "      <td>424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2022-02-12</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2022-02-13</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2022-02-14</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2022-02-15</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  acc_confirmed\n",
       "16 2022-01-08              3\n",
       "17 2022-01-09             25\n",
       "18 2022-01-10             35\n",
       "19 2022-01-11             68\n",
       "20 2022-01-12            101\n",
       "21 2022-01-13            142\n",
       "22 2022-01-14            181\n",
       "23 2022-01-15            214\n",
       "24 2022-01-16            294\n",
       "25 2022-01-17            312\n",
       "26 2022-01-18            326\n",
       "27 2022-01-19            340\n",
       "28 2022-01-20            348\n",
       "29 2022-01-21            354\n",
       "30 2022-01-22            359\n",
       "31 2022-01-23            360\n",
       "32 2022-01-24            361\n",
       "33 2022-01-25            361\n",
       "34 2022-01-26            362\n",
       "35 2022-01-27            366\n",
       "36 2022-01-28            371\n",
       "37 2022-01-29            375\n",
       "38 2022-01-30            386\n",
       "39 2022-01-31            393\n",
       "40 2022-02-01            405\n",
       "41 2022-02-02            414\n",
       "42 2022-02-03            418\n",
       "43 2022-02-04            420\n",
       "44 2022-02-05            422\n",
       "45 2022-02-06            423\n",
       "46 2022-02-07            424\n",
       "47 2022-02-08            424\n",
       "48 2022-02-09            424\n",
       "49 2022-02-10            424\n",
       "50 2022-02-11            424\n",
       "51 2022-02-12            425\n",
       "52 2022-02-13            425\n",
       "53 2022-02-14            425\n",
       "54 2022-02-15            425"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>acc_confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2022-01-08</td>\n",
       "      <td>2.559867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2022-01-09</td>\n",
       "      <td>18.950918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2022-01-10</td>\n",
       "      <td>38.178459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2022-01-11</td>\n",
       "      <td>67.008461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2022-01-12</td>\n",
       "      <td>106.025978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2022-01-13</td>\n",
       "      <td>139.991333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2022-01-14</td>\n",
       "      <td>175.684433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2022-01-15</td>\n",
       "      <td>212.721680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2022-01-16</td>\n",
       "      <td>279.449432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2022-01-17</td>\n",
       "      <td>310.587738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2022-01-18</td>\n",
       "      <td>328.266083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2022-01-19</td>\n",
       "      <td>336.075409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2022-01-20</td>\n",
       "      <td>348.081696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2022-01-21</td>\n",
       "      <td>357.108276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2022-01-22</td>\n",
       "      <td>361.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2022-01-23</td>\n",
       "      <td>359.837311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2022-01-24</td>\n",
       "      <td>360.068268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2022-01-25</td>\n",
       "      <td>360.265137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2022-01-26</td>\n",
       "      <td>359.032196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2022-01-27</td>\n",
       "      <td>365.098602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2022-01-28</td>\n",
       "      <td>370.901276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2022-01-29</td>\n",
       "      <td>375.626831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2022-01-30</td>\n",
       "      <td>385.902252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2022-01-31</td>\n",
       "      <td>397.399414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>408.773315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2022-02-02</td>\n",
       "      <td>418.533234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2022-02-03</td>\n",
       "      <td>418.876251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2022-02-04</td>\n",
       "      <td>422.686798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2022-02-05</td>\n",
       "      <td>426.917725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2022-02-06</td>\n",
       "      <td>424.160248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2022-02-07</td>\n",
       "      <td>423.289764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2022-02-08</td>\n",
       "      <td>424.899536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2022-02-09</td>\n",
       "      <td>424.806793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2022-02-10</td>\n",
       "      <td>425.970245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2022-02-11</td>\n",
       "      <td>427.137817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2022-02-12</td>\n",
       "      <td>428.187256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2022-02-13</td>\n",
       "      <td>429.126740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2022-02-14</td>\n",
       "      <td>429.899261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2022-02-15</td>\n",
       "      <td>430.528534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  acc_confirmed\n",
       "9  2022-01-08       2.559867\n",
       "10 2022-01-09      18.950918\n",
       "11 2022-01-10      38.178459\n",
       "12 2022-01-11      67.008461\n",
       "13 2022-01-12     106.025978\n",
       "14 2022-01-13     139.991333\n",
       "15 2022-01-14     175.684433\n",
       "16 2022-01-15     212.721680\n",
       "17 2022-01-16     279.449432\n",
       "18 2022-01-17     310.587738\n",
       "19 2022-01-18     328.266083\n",
       "20 2022-01-19     336.075409\n",
       "21 2022-01-20     348.081696\n",
       "22 2022-01-21     357.108276\n",
       "23 2022-01-22     361.250000\n",
       "24 2022-01-23     359.837311\n",
       "25 2022-01-24     360.068268\n",
       "26 2022-01-25     360.265137\n",
       "27 2022-01-26     359.032196\n",
       "28 2022-01-27     365.098602\n",
       "29 2022-01-28     370.901276\n",
       "30 2022-01-29     375.626831\n",
       "31 2022-01-30     385.902252\n",
       "32 2022-01-31     397.399414\n",
       "33 2022-02-01     408.773315\n",
       "34 2022-02-02     418.533234\n",
       "35 2022-02-03     418.876251\n",
       "36 2022-02-04     422.686798\n",
       "37 2022-02-05     426.917725\n",
       "38 2022-02-06     424.160248\n",
       "39 2022-02-07     423.289764\n",
       "40 2022-02-08     424.899536\n",
       "41 2022-02-09     424.806793\n",
       "42 2022-02-10     425.970245\n",
       "43 2022-02-11     427.137817\n",
       "44 2022-02-12     428.187256\n",
       "45 2022-02-13     429.126740\n",
       "46 2022-02-14     429.899261\n",
       "47 2022-02-15     430.528534"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_forecast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 特征重要性\n",
    "- 应用Permutation Importance计算特征重要性\n",
    "  - 随机打乱特征，计算MAE。特征越重要，MAE变化越明显\n",
    "  - 重复多次"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cal_mae(series1, series2):\n",
    "    a = np.array(list(series1))\n",
    "    b = np.array(list(series2))\n",
    "    return np.mean(np.abs(a-b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAE = True\n",
    "RMSE = False\n",
    "EPOCHES = 200\n",
    "\n",
    "features_mae= np.zeros(shape=len(cols)+1,dtype=np.float64)\n",
    "features_mae"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- baseline mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.7183695817605042\n"
     ]
    }
   ],
   "source": [
    "# compute baseline mae\n",
    "# take unshuffled feature data as baseline\n",
    "baseline_mae = cal_mae(original['acc_confirmed'], df_forecast['acc_confirmed'])\n",
    "# baseline_mae = np.mean(np.abs( original['acc_confirmed']-df_forecast['acc_confirmed']))   # wrong method!\n",
    "# results.append({'feature':'baseline','mae':baseline_mae})\n",
    "print(baseline_mae)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- feature mae\n",
    "  > 双层for循环没有运用加速，节省时间的话建议调整EPOCHE再测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute feature mae\n",
    "for epoch in range(EPOCHES):\n",
    "    for k in range(len(cols)):\n",
    "        # randomly shuffle feature k\n",
    "        save_col = trainX[:,:,k].copy()\n",
    "        # np.random.seed(int(time.time()))\n",
    "        np.random.shuffle(trainX[:,:,k])\n",
    "                \n",
    "        # predict using shuffled feature\n",
    "        shuffled_prediction = model.predict(trainX[-n_days_for_prediction:], verbose=0)\n",
    "        shuffled_prediction_copies = np.repeat(shuffled_prediction, df_for_training.shape[1], axis=-1)\n",
    "        shuffled_y_pred_future = scaler.inverse_transform(shuffled_prediction_copies)[:,0]\n",
    "\n",
    "        # convert timestamp to date\n",
    "        shuffled_forecast_dates = []\n",
    "        for time_i in predict_period_dates:\n",
    "            shuffled_forecast_dates.append(time_i.date())\n",
    "            \n",
    "        # locate the range of dates\n",
    "        shuffled_df_forecast = pd.DataFrame({'date':np.array(shuffled_forecast_dates), 'acc_confirmed':shuffled_y_pred_future})\n",
    "        shuffled_df_forecast['date']=pd.to_datetime(shuffled_forecast_dates)\n",
    "        shuffled_df_forecast = shuffled_df_forecast.loc[shuffled_df_forecast['date'] >= '2022-01-08']\n",
    "\n",
    "        # compute mae\n",
    "        # shuffled_mae = np.mean(np.abs( original['acc_confirmed']-shuffled_df_forecast['acc_confirmed'] ))\n",
    "        shuffled_mae = cal_mae(original['acc_confirmed'], shuffled_df_forecast['acc_confirmed'])\n",
    "        features_mae[k] += shuffled_mae\n",
    "        # results.append({'feature':cols[k],'mae':shuffled_mae})\n",
    "        trainX[:,:,k] = save_col.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_mae/=EPOCHES\n",
    "features_mae[-1]=baseline_mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'feature': 'acc_confirmed', 'mae': 34.191470740660655},\n",
       " {'feature': 'in_hospital', 'mae': 12.430232660660394},\n",
       " {'feature': 'acc_cured', 'mae': 31.97030961916998},\n",
       " {'feature': 'loc', 'mae': 21.908127992385445},\n",
       " {'feature': 'catering', 'mae': 7.597432661887358},\n",
       " {'feature': 'community', 'mae': 16.815956128867008},\n",
       " {'feature': 'transportation', 'mae': 9.926500637303375},\n",
       " {'feature': 'shop', 'mae': 17.866518155611487},\n",
       " {'feature': 'work', 'mae': 7.510473881501421},\n",
       " {'feature': 'entertainment', 'mae': 10.465250030950113},\n",
       " {'feature': 'education', 'mae': 16.33004162005888},\n",
       " {'feature': 'hotel', 'mae': 12.17015577714066},\n",
       " {'feature': 'medical', 'mae': 27.651375134425276},\n",
       " {'feature': 'sight', 'mae': 8.490472139920945},\n",
       " {'feature': 'baseline', 'mae': 2.7183695817605042}]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = []\n",
    "for k in range(len(cols)):\n",
    "    results.append({'feature':cols[k], 'mae':features_mae[k]})\n",
    "results.append({'feature':'baseline', 'mae':baseline_mae})\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_results = pd.DataFrame(results)\n",
    "df_results = df_results.sort_values('mae')\n",
    "plt.figure(figsize=(10,20))\n",
    "plt.barh(np.arange(len(cols)+1),df_results.mae)\n",
    "plt.yticks(np.arange(len(cols)+1),df_results.feature.values)\n",
    "plt.title('LSTM Feature Importance',size=16)\n",
    "plt.ylim((-1,len(cols)+1))\n",
    "plt.plot([baseline_mae,baseline_mae],[-1,len(cols)+1], '--', color='orange', label=f'Baseline MAE={baseline_mae:.3f}')\n",
    "plt.xlabel('MAE feature permuted',size=14)\n",
    "plt.ylabel('Feature',size=14)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>mae</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>baseline</td>\n",
       "      <td>2.718370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>work</td>\n",
       "      <td>7.510474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>catering</td>\n",
       "      <td>7.597433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>sight</td>\n",
       "      <td>8.490472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>transportation</td>\n",
       "      <td>9.926501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>entertainment</td>\n",
       "      <td>10.465250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>hotel</td>\n",
       "      <td>12.170156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>in_hospital</td>\n",
       "      <td>12.430233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>education</td>\n",
       "      <td>16.330042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>community</td>\n",
       "      <td>16.815956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>shop</td>\n",
       "      <td>17.866518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>loc</td>\n",
       "      <td>21.908128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>medical</td>\n",
       "      <td>27.651375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>acc_cured</td>\n",
       "      <td>31.970310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>acc_confirmed</td>\n",
       "      <td>34.191471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           feature        mae\n",
       "14        baseline   2.718370\n",
       "8             work   7.510474\n",
       "4         catering   7.597433\n",
       "13           sight   8.490472\n",
       "6   transportation   9.926501\n",
       "9    entertainment  10.465250\n",
       "11           hotel  12.170156\n",
       "1      in_hospital  12.430233\n",
       "10       education  16.330042\n",
       "5        community  16.815956\n",
       "7             shop  17.866518\n",
       "3              loc  21.908128\n",
       "12         medical  27.651375\n",
       "2        acc_cured  31.970310\n",
       "0    acc_confirmed  34.191471"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'feature': 'acc_confirmed', 'percentage_mae': 1157.7933100074326},\n",
       " {'feature': 'in_hospital', 'percentage_mae': 357.26794266915584},\n",
       " {'feature': 'acc_cured', 'percentage_mae': 1076.0839965868427},\n",
       " {'feature': 'loc', 'percentage_mae': 705.9289707839149},\n",
       " {'feature': 'catering', 'percentage_mae': 179.48490568993984},\n",
       " {'feature': 'community', 'percentage_mae': 518.6044841620267},\n",
       " {'feature': 'transportation', 'percentage_mae': 265.16376227527724},\n",
       " {'feature': 'shop', 'percentage_mae': 557.2512536739229},\n",
       " {'feature': 'work', 'percentage_mae': 176.28597420654606},\n",
       " {'feature': 'entertainment', 'percentage_mae': 284.98260505742115},\n",
       " {'feature': 'education', 'percentage_mae': 500.7292654254546},\n",
       " {'feature': 'hotel', 'percentage_mae': 347.70055767247345},\n",
       " {'feature': 'medical', 'percentage_mae': 917.2044051683858},\n",
       " {'feature': 'sight', 'percentage_mae': 212.3369315522667},\n",
       " {'feature': 'baseline', 'percentage_mae': 0}]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentage_feature_mae = (features_mae-baseline_mae)/baseline_mae*100\n",
    "percentage_results = []\n",
    "for k in range(len(cols)):\n",
    "    percentage_results.append({'feature':cols[k], 'percentage_mae':percentage_feature_mae[k]})\n",
    "percentage_results.append({'feature':'baseline', 'percentage_mae':0})\n",
    "percentage_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_percentage_results = pd.DataFrame(percentage_results)\n",
    "df_percentage_results = df_percentage_results.sort_values('percentage_mae')\n",
    "plt.figure(figsize=(10,20))\n",
    "plt.barh(np.arange(len(cols)+1),df_percentage_results.percentage_mae)\n",
    "plt.yticks(np.arange(len(cols)+1),df_percentage_results.feature.values)\n",
    "plt.title('LSTM Feature Importance',size=16)\n",
    "plt.ylim((-1,len(cols)+1))\n",
    "plt.plot([0,0],[-1,len(cols)+1], '--', color='orange', label=f'Baseline={0:.3f}')\n",
    "plt.xlabel('MAE feature permuted',size=14)\n",
    "plt.ylabel('Feature',size=14)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> - 主要用以预测的累计确诊人数占主要作用\n",
    "> - 其次为累计治愈人数、居住情况、娱乐活动、教育培训、医疗检测等\n",
    "> - 餐饮、交通、感染住院、购物、景点等作用不明显\n",
    "\n",
    "- 局部分析（某一时间段的作用）\n",
    "- 其他与模型松耦合的方法"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.13 ('pandemic_test')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
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